Replication Data for: Effectiveness of Social Norm-Based Nudge Interventions on Food Waste Reduction

Author:
ZHANG Yuchi

Supervisor:
UWASU Michinori

Description:
This replication package contains the dataset and Stata code used in the analysis of a field experiment examining the effectiveness of social norm–based informational nudges on food waste reduction in a university dining facility in China.

Data Description:
The dataset records daily measurements of edible plate waste collected from two university dining halls (treatment and control) during weekday lunch periods over a four-week experimental period (October 8–November 1, 2024).

Each observation represents one day of data collection for one dining facility.

Variables included:
- totalweight: Total weight of edible food waste collected (kg)
- numplate: Number of plates sampled
- averageweight: Average food waste per plate (grams)
- interv: Treatment indicator (0 = control group, 1 = treatment group)
- date: Calendar date of data collection
- coltime: Time required to collect the sample plates (minutes)
- weekday: Weekday indicator (1 = Monday … 5 = Friday)

Missing Observations:
The experiment was originally planned for 40 observations. Due to scheduling constraints during the data collection period, three observations were not collected. The dataset therefore contains 37 observations.

Files Included:
1. original_data.csv
   Raw dataset used in the empirical analysis.

2. original_data.dta
   Raw dataset in Stata dataset format.

3. field_experiment_analysis.do
   Stata code used to replicate the statistical analysis in the paper.

4. Treated_data.dta
   Stata dataset after the treatments on the original dataset.

Instructions for Replication:
To reproduce the main regression results:

1. Open Stata.
2. Set the working directory to the folder containing this replication package.
3. Run the file "field_experiment_analysis.do".

The code will load the dataset and reproduce the regression results reported in the paper.

Contact:
For questions regarding the dataset, please contact:
zhangyuchi2022@gmail.com